code: def test_inverse_transform_dense():
      sel = StepSelector()
      Xinv_actual = sel.fit(X, y).inverse_transform(Xt)
      assert_array_equal(Xinv, Xinv_actual)
  
      # Check dtype matches
      assert (
          np.int32 == sel.inverse_transform(Xt.astype(np.int32)).dtype
      )
      assert (
          np.float32
          == sel.inverse_transform(Xt.astype(np.float32)).dtype
      )
  
      # Check 1d list and other dtype:
      names_inv_actual = sel.inverse_transform([feature_names_t])
      assert_array_equal(feature_names_inv, names_inv_actual.ravel())
  
      # Check wrong shape raises error
      with pytest.raises(ValueError):
          sel.inverse_transform(np.array([[1], [2]]))
  
file_path: scikit-learn/sklearn/feature_selection/tests/test_base.py
imports: import numpy
  import pytest
  from numpy.testing import assert_array_equal
  from sklearn.base import BaseEstimator
  from sklearn.feature_selection._base import SelectorMixin
  from sklearn.utils.fixes import CSC_CONTAINERS
label_desc: Use image node_id 3 for calling a global function with example usage: test_inverse_transform_dense() without return types